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Category: robotics/AI – Page 2

GDC’s Survey Says Over 50% Of Game Devs See Gen AI As Harmful

Each year, thousands of professionals contribute to GDC’s State of the Game Industry report, offering studios, investors, and creators a snapshot of where the market is headed.

This year’s survey gathered responses from more than 2,300 game industry professionals, including developers, producers, marketers, executives, and investors, covering topics such as layoffs, diversity and inclusion, business models, and generative AI. Just over half of respondents were based in the United States, with a disproportionate share coming from North America and Western Europe, meaning the survey is not fully representative of the global industry.

However, some of these findings may reflect broader global trends. You can feel the mood shifting around AI, with its use increasingly sparking backlash whenever it comes up, from Baldur’s Gate 3 controversies to “Microslop”.

Measuring metabolic flux in brain cancer patients with AI based digital twin

The study, published in Cell Metabolism, builds on previous research showing that some gliomas can be slowed down through the patient’s diet. If a patient isn’t consuming certain protein building blocks, called amino acids, then some tumors are unable to grow. However, other tumors can produce these amino acids for themselves, and can continue growing anyway. Until now, there was no easy way to tell which patients would benefit from dietary restrictions.

The digital twin’s ability to map metabolic activity in tumors also helped determine whether a drug that prevents tumors from producing a building block for replicating and repairing DNA would work, as some cells can obtain that molecule from their environments.

To overcome challenges in mapping tumor metabolism inside the brain, the team developed a computer-based “digital twin” that can predict how an individual patient’s brain tumor will react to each treatment.

“Typically, metabolic measurements during surgeries to remove tumors can’t provide a clear picture of tumor metabolism—surgeons can’t observe how metabolism varies with time, and labs are limited to studying tissues after surgery. By integrating limited patient data into a model based on fundamental biology, chemistry and physics, we overcame these obstacles,” said a co-corresponding author of the study.

The digital twin uses patient data obtained through blood draws, metabolic measurements of the tumor tissue and the tumor’s genetic profile. The digital twin then calculates the speed at which the cancer cells consume and process nutrients, known as metabolic flux.

“This is the first time a machine learning and AI-based approach has been used to measure metabolic flux directly in patient tumors,” said a co-first author of the study.

The researchers built a type of deep learning model called a convolutional neural network and trained it on synthetic patient data, generated based on known biology and chemistry and constrained by measurements from eight patients with glioma who were infused with labeled glucose during surgery. By comparing their computer models with different data from six of those patients, they found the digital twins could predict metabolic activity with high accuracy. In experiments conducted on mice, the team confirmed that the diet only slowed tumor growth in mice that the digital twin had identified as good candidates for the treatment.

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The $350 Million Gamble: Intel Seizes First-Mover Advantage in the High-NA EUV Era

As of January 2026, the global race for semiconductor supremacy has reached a fever pitch, centered on a massive, truck-sized machine that costs more than a fleet of private jets. ASML (NASDAQ: ASML) has officially transitioned its “High-NA” (High Numerical Aperture) Extreme Ultraviolet (EUV) lithography systems into high-volume manufacturing, marking the most significant shift in silicon fabrication in over a decade. While the industry grapples with the staggering $350 million to $400 million price tag per unit, Intel (NASDAQ: INTC) has emerged as the aggressive vanguard, betting its entire “IDM 2.0” turnaround strategy on being the first to operationalize these tools for the next generation of “Angstrom-class” processors.

The transition to High-NA EUV is not merely a technical upgrade; it is a fundamental reconfiguration of how the world’s most advanced AI chips are built. By enabling higher-resolution circuitry, these machines allow for the creation of transistors so small they are measured in Angstroms (tenths of a nanometer). For an industry currently hitting the physical limits of traditional EUV, this development is the “make or break” moment for the continuation of Moore’s Law and the sustained growth of generative AI compute.

Fruit fly ‘Fox’ neurons show how brains assign value to food

Why do we sometimes keep eating even when we’re full and other times turn down food completely? Why do we crave salty things at certain times, and sweets at other times? The answers, according to new neuroscience research at the University of Delaware, may lie in a tiny brain in an organism you might not expect.

Lisha Shao, assistant professor in the Department of Biological Sciences in the College of Arts and Sciences, has uncovered a neural network in the brains of fruit flies that represents a very early step in how the brain decides—minute by minute—whether a specific food is worth eating. The work was published in the journal Current Biology.

“Our goal is to understand how the brain assigns value—why sometimes eating something is rewarding and other times it’s not,” Shao said.

AI to predict the risk of cancer metastases

Metastasis remains the leading cause of death in most cancers, particularly colon, breast and lung cancer. Currently, the first detectable sign of the metastatic process is the presence of circulating tumor cells in the blood or in the lymphatic system. By then, it is already too late to prevent their spread. Furthermore, while the mutations that lead to the formation of the original tumors are well understood, no single genetic alteration can explain why, in general, some cells migrate and others do not.

“The difficulty lies in being able to determine the complete molecular identity of a cell – an analysis that destroys it – while observing its function, which requires it to remain alive,” explains the senior author. “To this end, we isolated, cloned and cultured tumor cells,” adds a co-first author of the study. “These clones were then evaluated in vitro and in a mouse model to observe their ability to migrate through a real biological filter and generate metastases.”

The analysis of the expression of several hundred genes, carried out on about thirty clones from two primary colon tumors, identified gene expression gradients closely linked to their migratory potential. In this context, accurate assessment of metastatic potential does not depend on the profile of a single cell, but on the sum of interactions between related cancer cells that form a group.

The gene expression signatures obtained were integrated into an artificial intelligence model developed by the team. “The great novelty of our tool, called ‘Mangrove Gene Signatures (MangroveGS)’, is that it exploits dozens, even hundreds, of gene signatures. This makes it particularly resistant to individual variations,” explains another co-first author of the study. After training, the model achieved an accuracy of nearly 80% in predicting the occurrence of metastases and recurrence of colon cancer, a result far superior to existing tools. In addition, signatures derived from colon cancer can also predict the metastatic potential of other cancers, such as stomach, lung and breast cancer.

After training, the model achieved an accuracy of nearly 80% in predicting the occurrence of metastases and recurrence of colon cancer, a result far superior to existing tools. In addition, signatures derived from colon cancer can also predict the metastatic potential of other cancers, such as stomach, lung and breast cancer.

Thanks to MangroveGS, tumor samples are sufficient: cells can be analysed and their RNA sequenced at the hospital, then the metastatic risk score quickly transmitted to oncologists and patients via an encrypted Mangrove portal that has analysed the anonymised data.

“This information will prevent the overtreatment of low-risk patients, thereby limiting side effects and unnecessary costs, while intensifying the monitoring and treatment of those at high risk,” adds the senior author. “It also offers the possibility of optimising the selection of participants in clinical trials, reducing the number of volunteers required, increasing the statistical power of studies, and providing therapeutic benefits to the patients who need it most.” ScienceMission sciencenewshighlights.

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A new flexible AI chip for smart wearables is thinner than a human hair

The promise of smart wearables is often talked up, and while there have been some impressive innovations, we are still not seeing their full potential. Among the things holding them back is that the chips that operate them are stiff, brittle, and power-hungry. To overcome these problems, researchers from Tsinghua University and Peking University in China have developed FLEXI, a new family of flexible chips. They are thinner than a human hair, flexible enough to be folded thousands of times, and incorporate AI.

A flexible solution

In a paper published in the journal Nature, the team details the design of their chip and how it can handle complex AI tasks, such as processing data from body sensors to identify health indicators, such as irregular heartbeats, in real time.

Tiny silicon structures compute with heat, achieving 99% accurate matrix multiplication

MIT researchers have designed silicon structures that can perform calculations in an electronic device using excess heat instead of electricity. These tiny structures could someday enable more energy-efficient computation. In this computing method, input data are encoded as a set of temperatures using the waste heat already present in a device.

The flow and distribution of heat through a specially designed material forms the basis of the calculation. Then the output is represented by the power collected at the other end, which is a thermostat at a fixed temperature.

The researchers used these structures to perform matrix vector multiplication with more than 99% accuracy. Matrix multiplication is the fundamental mathematical technique machine-learning models like LLMs utilize to process information and make predictions.

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